--- title: "Careers at Autolab · ML Infrastructure & Agents Roles" description: "Join Autolab, the autoresearch platform. Hiring in San Francisco: infrastructure, agent/harness and post-training ML engineers, and forward deployed roles." url: https://www.autolab.ai/careers.html --- # Work with Autolab Autolab builds autoresearch infrastructure: agents that accelerate model training by automating complex ML experiments and decision-making. We are hiring for our first roles. Every role offers competitive salary and meaningful equity. ### Infrastructure Engineer San Francisco, California · Full-time + − Build the distributed systems that run thousands of concurrent ML experiments: job orchestration, GPU scheduling, artifact storage, observability. WHAT YOU’LL DO - Design and operate the orchestration layer that schedules thousands of concurrent experiments across heterogeneous GPU fleets. - Build GPU scheduling that keeps clusters near full utilization, from a single lab 3090 to multi-node H100 clusters. - Own artifact storage for checkpoints, datasets, and logs: versioned, deduplicated, and fast to fetch. - Build the observability pipeline that ingests training telemetry at high volume without slowing runs down. - Make experiment launches reproducible: environments, dependencies, and data pinned by default. - Set the engineering practices, CI, deployment, on-call, that the team grows into. WHAT WE’RE LOOKING FOR - You have built and operated distributed systems in production: schedulers, queues, or storage. - Strong Python plus a systems language (Go, Rust, or C++). - Hands-on experience with cluster tooling such as Kubernetes, Slurm, or Ray. - You have run GPU or HPC workloads and know where they break. - Pragmatic by default: ship the simple version, measure, then harden. - Comfortable owning large areas with little process in an early-stage team. [Apply](https://forms.gle/LmXvrgc22B9tRtZj7) ### Harness & Agents Engineer San Francisco, California · Full-time + − Design the agent harness: tool interfaces, sandboxing, evaluation loops, and the control logic that lets agents plan and execute research autonomously. WHAT YOU’LL DO - Design the tool interfaces agents use to write code, launch experiments, and read results. - Build sandboxing that lets agents execute generated code safely against real training infrastructure. - Implement evaluation loops that measure whether agents make good research decisions, not just whether code runs. - Develop the control logic for long-horizon work: when to branch an experiment, kill a run, or escalate to a human. - Instrument agent trajectories so failures are debuggable and successes are repeatable. - Iterate on prompts, context assembly, and memory alongside the post-training team. WHAT WE’RE LOOKING FOR - You have built LLM agent systems or harnesses that ran against real workloads, not just demos. - Strong software engineering fundamentals and good taste in API design. - Experience with isolation and sandboxing: containers, VMs, or permission models. - You have written evals and know how easily they mislead. - Enough ML background to understand the experiments your agents are running. - You treat agent reliability as an engineering problem, not a prompting trick. [Apply](https://forms.gle/LmXvrgc22B9tRtZj7) ### Post-training ML Engineer San Francisco, California · Full-time + − Own post-training for our agent models: RL and SFT pipelines, reward design, evals, and data engines built from experiment trajectories. WHAT YOU’LL DO - Own RL and SFT pipelines end to end: data, training, evaluation, and deployment. - Design reward signals for research decisions, where ground truth arrives hours or days after the action. - Build data engines that turn experiment trajectories into training data. - Run post-training experiments with proper baselines and ablations, and write up what you learn. - Build the eval suites that catch capability regressions before they ship. - Work with the harness team to close the loop between agent behavior and model updates. WHAT WE’RE LOOKING FOR - Hands-on post-training experience with LLMs: SFT, RLHF/RLAIF, DPO, or similar. - Strong PyTorch or JAX, and comfort working inside distributed training stacks. - Empirical rigor: you reach for a baseline and an ablation before an opinion. - You have felt reward hacking and eval overfitting firsthand and design against them. - Able to build the data tooling around training, not just the training loop. - Publications or shipped models are both welcome; neither is required. [Apply](https://forms.gle/LmXvrgc22B9tRtZj7) ### Forward Deployed Engineer San Francisco, California · Remote (US) · Full-time + − Embed with design partners (self-driving, robotics, CV teams), integrate Autolab into their training stacks, and feed what you learn back into the product. WHAT YOU’LL DO - Embed with design partners in self-driving, robotics, and computer vision, and get Autolab running in their training stacks. - Debug across the boundary: their training code, our agents, and the infrastructure in between. - Build the integrations, adapters, and examples that make the next deployment faster than the last. - Turn partner pain points into concrete, prioritized product feedback. - Be the partner’s technical point of contact from first call to production use. - Travel to partner sites when the problem is easier to solve in the same room. WHAT WE’RE LOOKING FOR - A strong generalist engineer who is productive in an unfamiliar codebase within days. - Real ML training experience: you have trained models, not just called APIs. - You communicate clearly with customers, including when the news is bad. - Bias to ownership: when something is broken at a partner, you fix it or find who can. - Comfortable with ambiguity, context switching, and occasional travel. - You enjoy being the person the product gets judged by. [Apply](https://forms.gle/LmXvrgc22B9tRtZj7) ### Chief of Staff San Francisco, California · Full-time + − Work directly with the founders on operations, hiring, partner relationships, and everything that keeps a fast-moving research company running. WHAT YOU’LL DO - Run hiring end to end: sourcing, pipelines, scheduling, references, and closing. - Own day-to-day operations: legal, finance, vendors, and workspace. - Manage partner and investor relationships alongside the founders. - Build the lightweight internal processes the team needs, and kill the ones it doesn’t. - Take whole problems off the founders’ plates and return them solved. - Keep the company’s commitments tracked and kept as the pace increases. WHAT WE’RE LOOKING FOR - Experience at an early-stage startup or another environment where you owned outcomes, not tasks. - Excellent writing: you can turn a messy discussion into a clear one-pager. - Organized under load: many threads, none dropped. - Comfortable with contracts, budgets, and people in the same afternoon. - Enough technical fluency to follow what an ML research company builds and why. - Discretion and judgment: you will see everything. [Apply](https://forms.gle/LmXvrgc22B9tRtZj7) Don’t see your role? Tell us what you’d build: [apply here](https://forms.gle/LmXvrgc22B9tRtZj7). autolab▮ © 2026 [autolab](https://www.autolab.ai/) [discord](https://discord.gg/2ZVZmE8Ppb)